Quantum oscillations and three-dimensional quantum Hall effect in ZrTe5_5

Recent experiments have reported a lot of spectacular transport properties in topological materials, such as quantum oscillations and three-dimensional (3D) quantum Hall effect (QHE) in ZrTe$_5$. In this paper, by using a strong topological insulator model to describe ZrTe$_5$, we study the magnetotransport property of the 3D system. With fixed carrier density, we find that there exists a deferring effect in the chemical potential, which favors distinguishing the saddle points of the inverted LLs. On the other hand, with fixed chemical potential, the features of 3D QHE are demonstrated and we attribute the underlying mechanisms to the interplay between Dirac fermions, magnetic field and impurity scatterings.Comment: 11 pages, 5 figure

Role of heparan sulfate proteoglycans in optic disc and stalk morphogenesis

Background Heparan sulfate proteoglycans (HSPG) are important for embryonic development via the regulation of gradient formation and signaling of multiple growth factors and morphogens. Previous studies have shown that Bmp/Shh/Fgf signaling are required for the regionalization of the optic vesicle (OV) and for the closure of the optic fissure (OF), the disturbance of which underlie ocular anomalies such as microphthalmia, coloboma and optic nerve hypoplasia. Results To study HSPG-dependent coordination of these signaling pathways during mammalian visual system development, we have generated a series of OV-specific mutations in the heparan sulfate (HS) N-sulfotransferase genes (Ndst1 and Ndst2) and HS O-sulfotransferase genes (Hs2st, Hs6st1 and Hs6st2) in mice. Interestingly, the resulting HS undersulfation still allowed for normal retinal neurogenesis and optic fissure closure, but led to defective optic disc and stalk development. The adult mutant animals further developed optic nerve aplasia/hypoplasia and displayed retinal degeneration. We observed that MAPK/ERK signaling was down-regulated in Ndst mutants, and consistent with this, HS-related optic nerve morphogenesis defects in mutant mice could partially be rescued by constitutive Kras activation. Conclusions These results suggest that HSPGs, depending on their HS sulfation pattern, regulate multiple signaling pathways in optic disc and stalk morphogenesis

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train DETR (UP-DETR) for object detection. Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation. Code and pre-training models: https://github.com/dddzg/up-detr.Comment: Accepted by CVPR 202

Cueing roles of new energy vehicle manufacturers’ technical capability and reputation in influencing purchase intention in China

Promoting new energy vehicle (NEV) is one of the main ways to save energy and reduce transport emissions, China has provided substantial subsidies for this since 2009. With the impending end of the subsidy policy ending in 2022, NEV manufacturers need to strengthen their competitiveness to continuously attract customers. Under the framework of cue utilization theory, this study takes NEV manufacturers’ technical capability as an intrinsic cue and reputation as an extrinsic cue to explore the mechanism in which two cues stimulate customers’ perceptions of travel quality and brand value, and subsequently motivate purchase intention. Based on a sample of 207 respondents from China, proposed hypotheses have been tested using Likert scale questionnaires through SPSS and AMOS. Structural equation modeling techniques were used to analyze the measurement scales and variable relationships. The results show that manufacturers’ reputation is more influential on both perceived travel quality and perceived brand value than technical capability; Technological turbulence plays a moderating role in the influence between perceived brand value and purchase intention. This article provides references for deepening related theories, and pragmatic insights for manufacturer strategic response and government policy making

Sex differences in the relationship of hip strength and functional performance to chronic ankle instability scores

BACKGROUND: While decreased hip abductor strength, functional performance, and self-reported instability scores have all been shown in association with CAI, any sex difference in the relationship between these indicators is unclear. This study was to determine whether sex differences are present in the relationship between these indicators in individuals with CAI. METHODS: Thirty-two women and twenty-nine men with unilateral CAI took part. Hip abductor strength and functional performance were respectively assessed using a hand-held dynamometer and the figure-8-hop test. All 61 participants scored the Cumberland Ankle Instability Tool (CAIT) for self-reported ankle instability. Independent sample t-tests and correlation analysis were conducted. RESULTS: Normalized hip abductor strength and functional performance measures for females were lower than for males. The self-reported ankle instability CAIT score, where higher values represent less instability, was significantly and positively correlated with both normalized hip abductor strength (p = 0.003) and functional performance (p = 0.001) on the affected side in females, but not in males (p = 0.361 and p = 0.192 respectively). CONCLUSIONS: Sex differences were observed in that there were significant relationships between normalized hip abductor strength, functional performance, and CAIT scores in female CAI participants, but not males, suggesting that CAI evaluation and rehabilitation strategies should be sex-specific. HIGHLIGHTS: In females with CAI, hip abductor strength and functional performance showed significant relationships with self-reported instability scores. Correspondingly, in clinical practice with individuals with CAI, evaluation criteria may be formulated according to these observed sex differences. Sex differences should be factored into the evaluation and treatment of CAI individuals. Hip strength assessment should be employed with CAI individuals. Hip strengthening and functional hopping may be recommended for the rehabilitation of CAI, especially in female patients

AHNAKs roles in physiology and malignant tumors

The AHNAK family currently consists of two members, namely AHNAK and AHNAK2, both of which have a molecular weight exceeding 600 kDa. Homologous sequences account for approximately 90% of their composition, indicating a certain degree of similarity in terms of molecular structure and biological functions. AHNAK family members are involved in the regulation of various biological functions, such as calcium channel modulation and membrane repair. Furthermore, with advancements in biological and bioinformatics technologies, research on the relationship between the AHNAK family and tumors has rapidly increased in recent years, and its regulatory role in tumor progression has gradually been discovered. This article briefly describes the physiological functions of the AHNAK family, and reviews and analyzes the expression and molecular regulatory mechanisms of the AHNAK family in malignant tumors using Pubmed and TCGA databases. In summary, AHNAK participates in various physiological and pathological processes in the human body. In multiple types of cancers, abnormal expression of AHNAK and AHNAK2 is associated with prognosis, and they play a key regulatory role in tumor progression by activating signaling pathways such as ERK, MAPK, Wnt, and MEK, as well as promoting epithelial-mesenchymal transition

Periodic Patterns and Energy States of Buckled Films on Compliant Substrates

Thin stiff films on compliant elastic substrates subject to equi-biaxial compressive stress states are observed to buckle into various periodic mode patterns including checkerboard, hexagonal and herringbone. An experimental setting in which these modes are observed and evolve is described. The modes are characterized and ranked by the extent to which they reduce the elastic energy of the film–substrate system relative to that of the unbuckled state over a wide range of overstress. A new mode is identified and analyzed having nodal lines coincident with an equilateral triangular pattern. Two methods are employed to ascertain the energy in the buckled state: an analytical upper-bound method and a full numerical analysis. The upper-bound is shown to be reasonably accurate to large levels of overstress. For flat films, except at small states of overstress where the checkerboard is preferred, the herringbone mode has the lowest energy, followed by the checkerboard, with the hexagonal, triangular, and one-dimensional modes lowering the energy the least. At low overstress, the hexagonal mode is observed in the experiments not the square mode. It is proposed that a slight initial curvature of the film may play role in selecting the hexagonal pattern accompanied by a detailed analysis. An intriguing finding is that the hexagonal and triangular modes have the same energy in the buckled state and, moreover, a continuous transition between these modes exists involving a linear combination of the two modes with no change in energy. Experimental observations of various periodic modes are discussed with reference to the energy landscape. Discrepancies between observations and theory are identified and open issues are highlighted.Engineering and Applied Science

Concussion classification via deep learning using whole-brain white matter fiber strains

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deep learning and machine learning classifiers consistently outperformed all scalar injury metrics across all performance categories in cross-validation (e.g., average accuracy of 0.844 vs. 0.746, and average area under the receiver operating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testing dataset). Nevertheless, deep learning achieved the best cross-validation accuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 for SVM and RF, respectively). These findings demonstrate the superior performances of deep learning in concussion prediction, and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.Comment: 18 pages, 7 figures, and 4 table